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Liberating the Biometric Menagerie Through Score Normalization Improvements

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posted on 2013-07-15, 00:00 authored by Jeffrey Richard Paone
The biometric menagerie, or biometric zoo, is a classification system used to label the matching tendencies of a given subject?s biometric signature. These tendencies may include matching their own signatures poorly or matching other subjects? signa- tures better than their own. Several experiments show the biometric menagerie to be an unstable classification system where subjects frequently change class labels. In an attempt to improve the stability of the biometric menagerie, existing score normal- ization techniques are expanded to create Covariate F-Normalization (CovF-Norm). When the normalization methods are applied to the biometric menagerie, the classifi- cation system remains unstable and unreliable for practical use with subject-specific thresholding. The new normalization method, CovF-Norm, is also shown to be algo- rithm independent and data set independent unlike the biometric menagerie which is dependent on both the algorithm and data set. CovF-Norm is shown to significantly improve performance when compared to the standard F-Normalization technique?s equal error rate.

History

Date Modified

2017-06-05

Defense Date

2013-06-19

Research Director(s)

Dr. Patrick Flynn

Committee Members

Dr. Jesus Izaguirre Dr. Kevin Bowyer Dr. Nitesh Chawla

Degree

  • Doctor of Philosophy

Degree Level

  • Doctoral Dissertation

Language

  • English

Alternate Identifier

etd-07152013-100819

Publisher

University of Notre Dame

Program Name

  • Computer Science and Engineering (CSE)

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